The CINTIL-TreeBank (Branco et al., 2011) is a corpus of syntactic constituency trees of Portuguese texts composed of 10,039 sentences and 110,166 tokens taken from different sources and domains: news (8,861 sentences; 101,430 tokens), novels (399 sentences; 3,082 tokens). In addition, there are ...
The CINTIL-PropBank (Branco et al., 2012) is a corpus of sentences annotated with their constituency structure and semantic role tags, composed of 10,039 sentences and 110,166 tokens taken from different sources and domains: news (8,861 sentences; 101,430 tokens), and novels (399 sentences; 3,082...
The LT Corpus (Literary Corpus) contains approximately 1,781,083 running words of European and Brazilian Portuguese. It includes 70 copyright-free classics (61 Portugal and 9 from Brazil) published before 1940.
The corpus presented here is a collection of several tutorials and scientific papers in the field of Information Technology with 603 annotated definitions from Portuguese. The texts were collected from the Web at the beginning of the 2006 and they are organised in 32 files of three different sub-...
LX-AP was created from the translation of Almuhareb-Poesio (ap) benchmark (Almuhareb and Poesio, 2005). The original data set was created considering three aspects: POS, frequency and ambiguity. It contains 402 names from 21 categories of WordNet, with 13 to 21 names from each one of those categ...
CORP-ORAL is a spontaneous speech corpus for European Portuguese. It is the main output of two R&D projects: CORP-ORAL and ORAL-PHON. The data consist of unscripted and unprompted face-to-face dialogues between family, friends, colleagues and unacquainted participants. All recordings are orthogra...
The LX-ESSLLI 2008 data set was created from the ESSLLI 2008 Distributional Semantic Workshop shared-task set, made of 44 concrete nouns grouped in 6 semantic categories (4 animate and 2 inanimate). The grouping is done in an hierarchical way following the top 10 properties from the McRae (2005) ...
The test set described in was used as the basis for the assessment of word embeddings. An example entry in this data set would read: ‘Berlin Germany Lisbon Portugal’. With these four words relations – as in this example – one can test semantic analogies by using any of the possible combinations o...